System and Methods for Determination of Analyte Concentration Using Time Resolved Amperometry

ABSTRACT

This invention is a method for determining a concentration of an analyte. The steps include applying a potential excitation to a fluid sample containing an analyte, and measuring one or more currents associated with one or more time-segments. The method can also include calculating a final analyte concentration based on a first and second set of analyte concentrations, wherein each set of analyte concentration values is based on a first and second set of calibration data associated with first and second time-segments.

This application claims priority to U.S. Provisional Patent ApplicationNo. 60/952,076, filed Jul. 26, 2007, which is incorporated herein byreference in its entirety.

DESCRIPTION OF THE INVENTION

1. Field of the Invention

The present invention relates to the field of diagnostic testing systemsfor determining the concentration of an analyte in a solution and, moreparticularly, to systems and methods for measuring an analyteconcentration using interpolate time resolved amperometry.

2. Background of the Invention

The present disclosure relates to a biosensor system for measuring ananalyte in a bodily fluid, such as blood. The system includes a processand system for improved determination of analyte concentration over awide range of analyte concentrations.

Electrochemical sensors have long been used to detect or measure thepresence of substances in fluid samples. Electrochemical sensors includea reagent mixture containing at least an electron transfer agent (alsoreferred to as an “electron mediator”) and an analyte specificbio-catalytic protein (e.g. a particular enzyme), and one or moreelectrodes. Such sensors rely on electron transfer between the electronmediator and the electrode surfaces and function by measuringelectrochemical redox reactions. When used in an electrochemicalbiosensor system or device, the electron transfer reactions aremonitored via an electrical signal that correlates to the concentrationof the analyte being measured in the fluid sample.

The use of such electrochemical sensors to detect analytes in bodilyfluids, such as blood or blood-derived products, tears, urine, andsaliva, has become important, and in some cases, vital to maintain thehealth of certain individuals. In the health care field, people such asdiabetics, for example, must monitor a particular constituent withintheir bodily fluids. A number of systems are capable of testing a bodyfluid, such as, blood, urine, or saliva, to conveniently monitor thelevel of a particular fluid constituent, such as, cholesterol, proteins,and glucose. Patients suffering from diabetes, a disorder of thepancreas where insufficient insulin production prevents the properdigestion of sugar, have a need to carefully monitor their blood glucoselevels on a daily basis. Routine testing and controlling blood glucosefor people with diabetes can reduce their risk of serious damage to theeyes, nerves, and kidneys.

A number of systems permit people to conveniently monitor their bloodglucose levels. Such systems typically include a test strip where theuser applies a blood sample and a meter that “reads” the test strip todetermine the glucose level in the blood sample. An exemplaryelectrochemical biosensor is described in U.S. Pat. No. 6,743,635 ('635patent) which describes an electrochemical biosensor used to measureglucose level in a blood sample. The electrochemical biosensor system iscomprised of a test strip and a meter. The test strip includes a samplechamber, a working electrode, a counter electrode, and fill-detectelectrodes. A reagent layer is disposed in the sample chamber. Thereagent layer contains an enzyme specific for glucose, such as, glucoseoxidase, glucose dehydrogenase, and a mediator, such as, potassiumferricyanide or ruthenium hexamine. When a user applies a blood sampleto the sample chamber on the test strip, the reagents react with theglucose in the blood sample and the meter applies a voltage to theelectrodes to cause redox reactions. The meter measures the resultingcurrent that flows between the working and counter electrodes andcalculates the glucose level based on the current measurements.

In some instances, electrochemical biosensors may be adversely affectedby the presence of certain blood components that may undesirably affectthe measurement and lead to inaccuracies in the detected signal. Thisinaccuracy may result in an inaccurate glucose reading, leaving thepatient unaware of a potentially dangerous blood sugar level, forexample. As one example, the particular blood hematocrit level (i.e. thepercentage of the amount of blood that is occupied by red blood cells)can erroneously affect a resulting analyte concentration measurement.Another example can include various constituents affecting bloodviscosity, cell lysis, concentration of charged species, pH, or otherfactors that may affect determination of an analyte concentration. Forexample, under certain conditions temperature could affect analytereadings and calculations.

Variations in a volume of red blood cells within blood can causevariations in glucose readings measured with disposable electrochemicaltest strips. Typically, a negative bias (i.e., lower calculated analyteconcentration) is observed at high hematocrits, while a positive bias(i.e., higher calculated analyte concentration) is observed at lowhematocrits. At high hematocrits, for example, the red blood cells mayimpede the reaction of enzymes and electrochemical mediators, reduce therate of chemistry dissolution since there less plasma volume to solvatethe chemical reactants, and slow diffusion of the mediator. Thesefactors can result in a lower-than-expected glucose reading as lesscurrent is produced during the electrochemical process. Conversely, atlow hematocrits, less red blood cells may affect the electrochemicalreaction than expected, and a higher measured current can result. Inaddition, the blood sample resistance is also hematocrit dependent,which can affect voltage and/or current measurements.

Several strategies have been used to reduce or avoid hematocrit basedvariations on blood glucose. For example, test strips have been designedto incorporate meshes to remove red blood cells from the samples, orhave included various compounds or formulations designed to increase theviscosity of red blood cell and attenuate the affect of low hematocriton concentration determinations. Other test strips have included lysisagents and systems configured to determine hemoglobin concentration inan attempt to correct hematocrit. Further, biosensors have beenconfigured to measure hematocrit by measuring optical variations afterirradiating the blood sample with light, or measuring hematocrit basedon a function of sample chamber fill time. These methods have thedisadvantages of increasing the cost and complexity of test strips andmay undesirably increase the time required to determine an accurateglucose measurement.

In addition, alternating current (AC) impedance methods have also beendeveloped to measure electrochemical signals at frequencies independentof a hematocrit effect. Such methods suffer from the increased cost andcomplexity of advanced meters required for signal filtering andanalysis.

Accordingly, systems and methods for determining analyte concentrationare desired that overcome the drawbacks of current biosensors andimprove upon existing electrochemical biosensor technologies.

SUMMARY OF THE INVENTION

Some embodiments of this invention are directed to methods and systemsfor determining a concentration of an analyte using one or more sets ofcalibration data. Embodiments of this invention can use two or more setsof calibration data associated with two or more time-segments. Fluidsamples containing similar analyte concentrations but different samplematrix (e.g. different hematocrit values) can produce differentcalibration data. However, these calibration data were found to convergeover time under certain conditions. Generally, fluid samples containinglow analyte concentrations can converge faster than fluid samplescontaining high analyte concentrations. Based on this convergencebehavior, an analyte concentration can be more accurately determined bydynamically selecting an appropriate time-segment and a calibration dataassociated with the selected time-segment.

One embodiment consistent with the principles of this invention is amethod for analyzing an analyte described as follows. The steps includeapplying a potential excitation to a fluid sample containing an analyte,and measuring a first current during a first time-segment followingapplication of the potential excitation. The method also includesmeasuring a second current during a second time-segment followingapplication of the potential excitation, and calculating a plurality offirst analyte concentrations based on the first measured current and afirst set of calibration data associated with the first time-segment.Lastly, the method includes calculating a plurality of second analyteconcentrations based on the second measured current and a second set ofcalibration data associated with the second time-segment.

Another embodiment of this invention is directed to a system foranalyzing an analyte in a fluid sample. The system includes a set ofelectrodes configured to apply a potential excitation to a fluid samplecontaining an analyte. The system also includes a processor configuredto measure a first current during a first time-segment followingapplication of the potential excitation, and measure a second currentduring a second time-segment following application of the potentialexcitation. The processor is further configured to calculate a pluralityof first analyte concentrations based on the first measured current anda plurality of first calibration curves associated with the firsttime-segment and calculate a plurality of second analyte concentrationsbased on the second measured current and a plurality of secondcalibration curves associated with the second time-segment.

Another embodiment of this invention is directed to a computer readablemedia, wherein the media comprises a plurality of instructionsconfigured to direct a processor to measure a first current during afirst time-segment following application of a potential excitation,wherein the potential excitation is applied to a fluid sample containingan analyte, and measure a second current during a second time-segmentfollowing application of the potential excitation. The instructions alsodirect the processor to calculate a plurality of first analyteconcentrations based on the first measured current and a first set ofcalibration data associated with the first time-segment, and calculate aplurality of second analyte concentrations based on the second measuredcurrent and a second set of calibration data associated with the secondtime-segment.

Additional embodiments consistent with principles of the invention areset forth in the detailed description which follows or may be learned bypractice of methods or use of systems or articles of manufacturedisclosed herein. It is understood that both the foregoing generaldescription and the following detailed description are exemplary andexplanatory only, and are not restrictive of the invention as claimed.Additionally, it is to be understood that other embodiments may beutilized and that electrical, logical, and structural changes may bemade without departing form the spirit and scope of the presentinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments of theinvention and together with the description, serve to explain theprinciples of the invention. In the drawings:

FIG. 1A illustrates test media associated with an exemplary metersystem, according to an exemplary embodiment of the present disclosure.

FIG. 1B illustrates a test meter that can be used with test media,according to an exemplary embodiment of the present disclosure.

FIG. 1C illustrates another test meter that can be used with test media,according to an exemplary embodiment of the present disclosure.

FIG. 2A is a top plan view of a test strip, according to an exemplaryembodiment of the present disclosure.

FIG. 2B is a cross-sectional view of the test strip of FIG. 2A, takenalong line 2B-2B.

FIG. 3 depicts flow chart of a method of determining an analyteconcentration, according to an exemplary embodiment of the presentdisclosure.

FIG. 4A depicts a plurality of calibration curves on a graph of currentversus glucose concentration, according to an exemplary embodiment ofthe present disclosure.

FIG. 4B depicts a plurality of calibration curves on a graph of currentversus glucose concentration, according to an exemplary embodiment ofthe present disclosure.

FIG. 4C depicts two line-plots, according to an exemplary embodiment ofthe present disclosure.

FIG. 5 depicts a plurality of current decay curves on a graph of currentversus time, according to an exemplary embodiment of the presentdisclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the present embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

In accordance with an exemplary embodiment, a method of determining ananalyte concentration is described. Many industries have a commercialneed to monitor the concentration of particular analytes in variousfluids. The oil refining industry, wineries, and the dairy industry areexamples of industries where fluid testing is routine. In the healthcare field, people such as diabetics, for example, need to routinelymonitor analyte levels of their bodily fluids using biosensors. A numberof systems are available that allow people to test a physiological fluid(e.g. blood, urine, or saliva), to conveniently monitor the level of aparticular analyte present in the fluid, such as, for example, glucose,cholesterol, ketone bodies, or specific proteins. Such systems caninclude a meter configured to determine the analyte concentration and/ordisplay representative information to a user. In addition, such meteringsystems can incorporate disposable test strips configured for single-usetesting of a fluid sample.

While such metering systems have been widely adopted, some aresusceptible to inaccurate readings resulting from analyzing fluids ofdiffering properties. For example, blood glucose monitoring usingelectrochemical techniques can be highly dependent upon hematocritand/or temperature fluctuations. The present method reduces unwantedinfluences by measuring one or more current values associated with oneor more time-segments. Various mathematical techniques are then used tosolve for one or more variables, such as, for example, glucoseconcentration and hematocrit. The present disclosure provides methodsand systems for improved determination of analyte concentration.

FIG. 1A illustrates a diagnostic test strip 10, according to anexemplary embodiment of the present disclosure. Test strip 10 of thepresent disclosure may be used with a suitable test meter 100, 108, asshown in FIGS. 1B and 1C, configured to detect, and/or measure theconcentration of one or more analytes present in a sample solutionapplied to test strip 10. As shown in FIG. 1A, test strip 10 can begenerally planar and elongated in design. However, test strip 10 may beprovided in any suitable form including, for example, ribbons, tubes,tabs, discs, or any other suitable form. Furthermore, test strip 10 canbe configured for use with a variety of suitable testing modalities,including electrochemical tests, photochemical tests,electro-chemiluminescent tests, and/or any other suitable testingmodality.

Test strip 10 can be in the form of a generally flat strip that extendsfrom a proximal end 12 to a distal end 14. For purposes of thisdisclosure, “distal” refers to the portion of test strip 10 further fromthe fluid source (i.e. closer to the meter) during normal use, and“proximal” refers to the portion closer to the fluid source (e.g. afinger tip with a drop of blood for a glucose test strip) during normaluse. In some embodiments, proximal end 12 of test strip 10 may include asample chamber 52 configured to receive a fluid sample, such as, forexample, a blood sample. Sample chamber 52 and test strip 10 of thepresent specification can be formed using materials and methodsdescribed in commonly owned U.S. Pat. No. 6,743,635, which is herebyincorporated by reference in its entirety.

Test strip 10 can be any convenient size. For example, test strip 10 canmeasure approximately 35 mm long (i.e., from proximal end 12 to distalend 14) and approximately 9 mm wide. Proximal end 12 can be narrowerthan distal end 14 in order to assist the user in locating the openingwhere the blood sample is to be applied. Further, test meter 100, 108can be configured to operate with, and dimensioned to receive, teststrip 10.

Test meter 100, 108 may be selected from a variety of suitable testmeter types. For example, as shown in FIG. 1B, test meter 100 includes avial 102 configured to store one or more test strips 10. The operativecomponents of test meter 100 may be contained in a meter cap 104. Metercap 104 may contain electrical meter components, can be packaged withtest meter 100, and can be configured to close and/or seal vial 102.Alternatively, test meter 108 can include a monitor unit separated fromstorage vial, as shown in FIG. 1C. In some embodiments, meter 100 caninclude one or more circuits, processors, or other electrical componentsconfigured to perform one or more steps of the disclosed method ofdetermining an analyte concentration. Any suitable test meter may beselected to provide a diagnostic test using test strip 10 producedaccording to the disclosed methods.

Test Strip Configuration

FIGS. 2A and 2B show a test strip 10, in accordance with an exemplaryembodiment of the present disclosure. As shown in FIG. 2B, test strip 10can include a generally layered construction. Working upwardly from thebottom layer, test strip 10 can include a base layer 18 extending alongthe entire length of test strip 10. Base layer 18 can be formed from anelectrically insulating material that has a thickness sufficient toprovide structural support to test strip 10. For example, base layer 18can be a polyester material about 0.35 mm thick.

According to the illustrative embodiment, a conductive layer 20 can bedisposed on base layer 18. Conductive layer 20 includes a plurality ofelectrodes disposed on base layer 18 near proximal end 12, a pluralityof electrical contacts disposed on base layer 18 near distal end 14, anda plurality of conductive regions electrically connecting the electrodesto the electrical contacts. In the illustrative embodiment depicted inFIG. 2A, the plurality of electrodes includes a working electrode 22, acounter electrode 24, and a pair of fill-detect electrodes 28, 30. Asdescribed in detail below, the term “working electrode” refers to anelectrode at which an electrochemical oxidation and/or reductionreaction occurs, e.g., where an analyte, typically the electronmediator, is oxidized or reduced. “Counter electrode” refers to anelectrode paired with working electrode 22.

The electrical contacts at distal end 14 can correspondingly include aworking electrode contact 32, a proximal electrode contact 34, andfill-detect electrode contacts 36, 38. The conductive regions caninclude a working electrode conductive region 40, electricallyconnecting working electrode 22 to working electrode contact 32, acounter electrode conductive region 42, electrically connecting counterelectrode 24 to counter electrode contact 36, and fill-detect electrodeconductive regions 44, 46 electrically connecting fill-detect electrodes28, 30 to fill-detect contacts 36, 38. Further, the illustrativeembodiment is depicted with conductive layer 20 including an auto-onconductor 48 disposed on base layer 18 near distal end 14.

In addition to auto-on conductor 48, the present disclosure providestest strip 10 that includes electrical contacts near distal end 14 thatare resistant to scratching or abrasion. Such test strips can includeconductive electrical contacts formed of two or more layers ofconductive and/or semi-conductive material. Further, informationrelating to electrical contacts that are resistant to scratching orabrasion are described in co-owned U.S. patent application Ser. No.11/458,298 which is incorporated by reference herein in its entirety.

The next layer of test strip 10 can be a dielectric spacer layer 64disposed on conductive layer 20. Dielectric spacer layer 64 can becomposed of an electrically insulating material, such as polyester.Dielectric spacer layer 64 can be about 0.100 mm thick and coversportions of working electrode 22, counter electrode 24, fill-detectelectrodes 28, 30, and conductive regions 40-46, but in the illustrativeembodiment does not cover electrical contacts 32-38 or auto-on conductor48. For example, dielectric spacer layer 64 can cover substantially allof conductive layer 20 thereon, from a line just proximal of contacts 32and 34 all the way to proximal end 12, except for sample chamber 52extending from proximal end 12. In this way, sample chamber 52 candefine an exposed portion 54 of working electrode 22, an exposed portion56 of counter electrode 24, and exposed portions 60, 62 of fill-detectelectrodes 28, 30.

In some embodiments, sample chamber 52 can include a first opening 68 atproximal end 12 of test strip 10, and a second opening 86 for ventingsample chamber 52. Further, sample chamber 52 may be dimensioned and/orconfigured to permit, by capillary action, a blood sample to enterthrough first opening 68 and remain within sample chamber 52. Forexample, sample chamber 52 can be dimensioned to receive about 1micro-liter or less. For example, first sample chamber 52 can have alength (i.e., from proximal end 12 to distal end 70) of about 0.140inches, a width of about 0.060 inches, and a height (which can besubstantially defined by the thickness of dielectric spacer layer 64) ofabout 0.005 inches. Other dimensions could be used, however.

A cover 72, having a proximal end 74 and a distal end 76, can beattached to dielectric spacer layer 64 via an adhesive layer 78. Cover72 can be composed of an electrically insulating material, such aspolyester, and can have a thickness of about 0.1 mm. Additionally, thecover 72 can be transparent. Adhesive layer 78 can include a polyacrylicor other adhesive and have a thickness of about 0.013 mm. A break 84 inadhesive layer 78 can extend from distal end 70 of first sample chamber52 to an opening 86, wherein opening 86 can be configured to vent samplechamber 52 to permit a fluid sample to flow into sample chamber 52.Alternatively, cover 72 can include a hole (not shown) configured tovent sample chamber 52. It is also contemplated that various materials,surface coatings (e.g. hydrophilic and/or hydrophobic), or otherstructure protrusions and/or indentations at proximal end 12 may be usedto form a suitable sample reservoir.

As shown in FIG. 2B, a reagent layer 90 can be disposed in samplechamber 52. In some embodiments, reagent layer 90 can include one ormore chemical constituents to enable the level of glucose in the bloodsample to be determined electrochemically. Reagent layer 90 may includean enzyme specific for glucose, such as glucose oxidase or glucosedehydrogenase, and a mediator, such as potassium ferricyanide orruthenium hexamine. In other embodiments, other reagents and/or othermediators can be used to facilitate detection of glucose and otheranalytes contained in blood or other physiological fluids. In addition,reagent layer 90 may include other components, buffering materials(e.g., potassium phosphate), polymeric binders (e.g.,hydroxypropyl-methyl-cellulose, sodium alginate, microcrystallinecellulose, polyethylene oxide, hydroxyethylcellulose, and/or polyvinylalcohol), and surfactants (e.g., Triton X-100 or Surfynol 485). Forexample, an exemplary formulation contains 50-250 mM potassium phosphateat pH 6.75-7.50, 150-190 mM ruthenium hexamine, 3500-5000 U/mLPQQ-dependent glucose dehydrogenase, 0.5-2.0% polyethylene oxide,0.025-0.20% NATROSOL 250M (hydroxyethylcellulose), 0.675-2.5% Avicel(microcrystalline cellulose), 0.05-0.25% TRITON-X (surfactant) and2.5-5.0% trehalose.

In some embodiments, various constituents may be added to reagent layer90 to at least partially reduce unwanted bias of an analyte measurement.For example, various polymers, molecules, and/or compounds may be addedto reagent layer 90 to reduce cell migration and hence may increase theaccuracy of a measurement based on an electrochemical reaction. Also,one or more conductive components may be coated with a surface layer(not shown) to at least partially restrict cell migration onto the oneor more conductive components. These and other techniques known in theart may be used to reduce unwanted signal bias.

Although FIGS. 2A and 2B illustrate an illustrative embodiment of teststrip 10, other configurations, chemical compositions and electrodearrangements could be used. For example, fill-detect electrode 30 canfunction with working electrode 22 to perform a fill-detect feature, aspreviously described. Other configurations of electrodes on test strip10 are possible, such as, for example, a single fill-detect electrode,multiple fill-detect electrodes aligned in the y-axis (as opposed to thex-axis as shown in FIG. 2A), and/or multiple working electrodes.

In some embodiments, working electrode 22 and counter electrode 24 canbe spaced further apart. For example, this electrode pair may be spacedat a distance of 500 μm to 1000 μm such that a two-pulse measurementobtained from the electrode pair can be optimized for correction of theinfluence of hematocrit, temperature, or other factors.

Test Strip and Meter Operation

As previously described, test strip 10 can be configured for placementwithin meter 100, or similar device, configured to determine theconcentration of an analyte contained in a solution in contact with teststrip 10. Meter 100 can include electrical components, circuitry, and/orprocessors configured to perform various operations to determine analyteconcentration based on electrochemical techniques. For example, themetering system, such as meter 100 and associated test strip 10, may beconfigured to determine the glucose concentration of a blood sample. Insome embodiments, systems and methods of the present disclosure permitdetermination of blood glucose levels generally unaffected by bloodconstituents, hematocrit levels, and temperature.

In operation, the battery-powered meter 100 may stay in a low-powersleep mode when not in use. When test strip 10 is inserted into meter100, one or more electrical contacts at distal end 14 of test strip 10could form electrical connections with one or more correspondingelectrical contacts in meter 100. These electrical contacts may bridgeelectrical contacts in meter 100, causing a current to flow through aportion of the electrical contacts. Such a current flow can cause meter100 to “wake-up” and enter an active mode.

Meter 100 can read encoded information provided by the electricalcontacts at distal end 14. Specifically, the electrical contacts can beconfigured to store information, as described in U.S. patent applicationSer. No. 11/458,298. In particular, an individual test strip 10 caninclude an embedded code containing data associated with a lot of teststrips, or data particular to that individual strip. The embeddedinformation can represent data readable by meter 100. For example, amicroprocessor associated with meter 100 could access and utilize aspecific set of stored calibration data specific to an individual teststrip 10 and/or a manufactured lot test strips 10. Individual teststrips 10 may be calibrated using standard solutions, and associatedcalibration data could be applied to test strips 10 of the same orsimilar lots of manufactured test strips 10.

In some embodiments, “lot specific” calibration information can beencoded on a code chip accompanying a vial of strips, or coded directlyonto one or more test strips 10 manufactured in a common lot of teststrips. Lot calibration can include any suitable process for calibratingtest strip 10 and/or meter 100. For example, calibration can includeapplying at the factory a standard solution to one or more test strips10 from a manufacturing lot, wherein the standard solution can be asolution of known glucose concentration, hematocrit, temperature, or anyother appropriate parameter associated with the solution. Followingapplication of the standard solution, one or more pulses can be appliedto test strip 10, as described below. Calibration data may then bedetermined by correlating various measurements to be determined by themeter 100 during use by the patient with one or more parametersassociated with the standard solution. For example, a measured currentmay be correlated with a glucose concentration, or a voltage correlatedwith hematocrit. Such calibration data, that can vary from lot to lotwith the performance of the test strips, may then be stored on teststrip 10 and/or meter 100, and used to determine analyte concentrationof an analyte sample, as described below.

Test strip 10 can be tested at any suitable stage during a manufacturingprocess. Also, a test card (not shown) could be tested during anysuitable stage of a manufacturing process, as described in co-owned U.S.patent application Ser. No. 11/504,710 which is incorporated byreference herein in its entirety. Such testing of test strip 10 and/orthe test card can permit determination and/or encoding of calibrationdata at any suitable stage during a manufacturing process. For example,calibration data associated with methods of the present disclosure canbe encoded during the manufacturing process.

In operation meter 100 can be configured to identify a particular testto be performed or provide a confirmation of proper operating status.Also, calibration data pertaining to the strip lot, for either theanalyte test or other suitable test, could be otherwise encoded orrepresented, as described above. For example, meter 100 can identify theinserted strip as either test strip 10 or a check strip (not shown)based on the particular code information.

If meter 100 detects test strip 10, it may perform a test stripsequence. The test strip sequence may confirm proper functioning of oneor more components of test strip 10. For example, meter 100 couldvalidate the function of working electrode 22, counter electrode 24,and, if included, the fill-detect electrodes, by confirming that thereare no low-impedance paths between any of these electrodes. If theelectrodes are valid, meter 100 could provide an indication to the userthat a sample may be applied to test strip 10.

If meter 100 detects a check strip, it may perform a check stripsequence. The system may also include a check strip configured toconfirm that the instrument is electrically calibrated and functioningproperly. The user may insert the check strip into meter 100. Meter 100may then receive a signal from the check strip to determine if meter 100is operating within an acceptable range.

In other embodiments, test strip 10 and/or meter 100 may be configuredto perform a calibration process based on a standard solution, alsotermed a control solution. The control solution may be used toperiodically test one or more functions of meter 100. For example, acontrol solution may include a solution of known electrical properties,and an electrical measurement of the solution may be performed by meter100. Upon detecting the presence of a control solution, meter 100 canperform an operational check of test strip 10 functionality to verifymeasurement integrity. For example, the read-out of meter 100 may becompared to a known glucose value of the solution to confirm that meter100 is functioning to an appropriate accuracy. In addition, any dataassociated with a measurement of a control solution may be processed,stored or displayed using meter 100 differently to any data associatedwith a glucose measurement. Such different treatment of data associatedwith the control solution may permit meter 100, or user, to distinguisha glucose measurement, or may permit exclusion of any controlmeasurements when conducting any mathematical analysis of glucosemeasurements.

Analyte Concentration Determination

Meter 100 can be configured to apply a signal to test strip 10 todetermine a concentration of an analyte contained in a solutioncontacting test strip 10. In some cases, the signal can be appliedfollowing a determination that sample chamber 52 of test strip 10contains a sufficient quantity of fluid sample. To determine thepresence of sufficient fluid, meter 100 can apply a detect voltagebetween any suitably configured electrodes, such as, for example,fill-detect electrodes. The detect voltage can detect the presence ofsufficient quantity of fluid (e.g. blood) within sample chamber 52 bydetecting a current flow between the fill-detect electrodes. Inaddition, to determine that the fluid sample has traversed reagent layer90 and mixed with the chemical constituents in reagent layer 90, meter100 may apply a fill-detect voltage to the one or more fill-detectelectrodes and measure any resulting current. If the resulting currentreaches a sufficient level within a predetermined period of time, meter100 can indicate to a user that adequate sample is present. Meter 100can also be programmed to wait for a predetermined period of time afterinitially detecting the blood sample to allow the blood sample to reactwith reagent layer 90. Alternatively, meter 100 can be configured toimmediately begin taking readings in sequence.

Meter 100 can be configured to apply various signals to test strip 10.For example, an exemplary fluid measurement sequence could includeamperometry, wherein an assay voltage is applied between working andcounter electrodes 22, 24 of test strip 10. The magnitude of the assayvoltage can include any suitable voltage, and could be approximatelyequal to the redox potential of constituents of reagent layer 90.Following application of an assay voltage, also termed potentialexcitation, meter 100 could be configured to measure one or more currentvalues between working and counter electrodes 22, 24. Such a measuredcurrent can be mathematically related to the concentration of analyte inthe fluid sample, such as, for example, glucose concentration in a bloodsample.

For example, one or more constituents of reagent layer 90 may react withglucose present in a blood sample such that glucose concentration may bedetermined using electrochemical techniques. Suitable enzymes of reagentlayer 90 (e.g. glucose oxidase or glucose dehydrogenase) could reactwith blood glucose. Glucose could be oxidized to form gluconic acid,which may in turn reduce a suitable mediator, such as, for example,potassium ferricyanide or ruthenium hexamine. Voltage applied to workingelectrode 22 may oxidize the ferrocyanide to form ferricyanide, andgenerating a current proportional to the glucose concentration of theblood sample.

As previously discussed, measurements of analyte concentration using abiosensor may be inaccurate due to unwanted effects of various bloodcomponents. For example, the hematocrit level (i.e. the percentage ofblood occupied by red blood cells) of blood can erroneously affect ameasurement of analyte concentration. In order to reduce inaccuraciesassociated with a determination of analyte concentration, it may beadvantageous to use multiple sets of calibration data. Such calibrationdata can reduce errors due to hematocrit or other factors that mayadversely affect analyte concentration determination.

Exemplary embodiments disclosed herein use multiple sets of calibrationdata to permit more precise determinations of analyte concentration overa wider range of analyte concentrations than can be achieved usingtraditional techniques. The influence of hematocrit, temperature, bloodconstituents, and other factors that may adversely affect determinationof blood glucose concentration can be reduced using techniques thatemploy multiple calibration data. The precision and/or accuracy ofmonitoring blood glucose levels using biosensors may be improved usingthe method or systems of the present disclosure. Different sets ofcalibration data can be associated with different ranges of analyteconcentrations, currents, voltages, or sampling times. In particular, aset of calibration data can include one or more of a calibration curve,a lookup table, a data array, or a mathematical equation.

One exemplary embodiment is directed to a method that includescalculating an analyte concentration based on two measured currents andtwo sets of calibration curves. Each set of calibration curves caninclude a plurality of calibration curves that may be associated with atime-segment, as explained in detail below. For example, at a timeduring the first time-segment, a first analyte concentration can becalculated using two or more calibration curves associated with thefirst time-segment. If the calculated first analyte concentration iswithin a pre-determined concentration range associated with the firsttime-segment, current measurement can stop, analyte concentration can bedetermined and the result displayed. If the calculated analyteconcentration is outside the pre-determined range, the currentmeasurement can continue to a time during a second time-segment. At thattime, a second analyte concentration can be calculated using parametersassociated with a second time-segment. If the second analyteconcentration is about equal to the first analyte concentration, currentmeasurement can stop, a final analyte concentration can be determinedand the result displayed. Otherwise, the method can continue todetermine a final analyte concentration based on a plurality of firstanalyte concentrations and a plurality of second analyte concentrations.As explained in detail below, various mathematical algorithms can beused to determine a final analyte concentration. For example, a firstplurality of analyte concentrations may provide an initial glucoseconcentration and a second plurality of analyte concentrations mayprovide a different glucose concentration based on a presumed hematocritlevel. The difference in calculated glucose concentration could be used,along with two or more calibration curves, to refine the calculationusing interpolative and/or extrapolative techniques. In someembodiments, the calculation may be refined using iterative techniquesto converge to a final analyte concentration. Such methods permit use ofmultiple sets of calibration data associated with time, current,voltage, analyte concentrations, or other parameters, to improve theprecious or accuracy of electrochemical techniques used to determine ananalyte concentration.

In some embodiments, analyte concentration may be determined by firstapplying a potential excitation to a fluid sample in contact with teststrip 10. An applied potential can include any suitable voltage signal,such as, for example, signals with constant, variable, or pulse-trainvoltages. Meter 100 may then measure a current value associated with thepotential excitation, as previously described.

In some embodiments, a current may be measured at one or moretime-points. A time-point can include a discrete time followingapplication of a potential excitation. For example, a first current canbe measured at a first time-point of 0.1 seconds, and a second currentcan be measured at a second time-point 0.2 seconds. The first time-pointcan occur 0.1 seconds following the application of the potential, andthe second time-point can occur 0.2 seconds following the application ofthe potential. In some cases, a plurality of current values can bemeasured at any number of time-points following the application of apotential excitation.

Time-points can include irregular or regular time periods, and caninclude any suitable sampling rate. For example, the sampling rate couldbe 10 Hz, and in other embodiments the sampling rate could be 0.1, 1,100, or 1000 Hz. In other embodiments, time-points could be sampled atnon-constant sampling rates. For example, time-points could be sampledat increasing, decreasing, or non-uniform sampling rates.

In some embodiments, current values can be measured over a plurality oftime-segments, wherein a time-segment can include a series oftime-points or span a specific time period. For example, a firsttime-segment could include any number of time-points up to about sixseconds, and a second time-segment could include any number oftime-points following about six seconds. In other embodiments, a firsttime-segment could include a time period less than about six seconds,and a second time-segment could include a time period greater than aboutsix seconds. In yet other embodiments, a first time-segment could beless than about twenty seconds, and a second time-segment could be anytime period following the first time-period.

The time period of any time-segment could vary depending upon variousfactors, including configuration of test strip 10, meter 100, bodilysample analyzed, or reagents of test strip 10. For example, a firsttime-segment could span a time from about zero to about two to tenseconds following application of a potential excitation. Further, insome embodiments the end of a time-segment may coincide with thebeginning of the next time-segment, while in other embodiments, a priorand later time segment may not coincide. For example, a firsttime-segment may span the time period from about zero to ten seconds anda second time-segment may span the time period from about two tofourteen seconds.

Current values measured in the first time-segment could include one ormore currents measured at 0.1, 0.2, 1.6, 2.0, 3.4, or 5.99 seconds, orat any other suitable times. Current values measured in the secondtime-segment could include one or more currents measured at 6.2, 6.63,7.0, or 9.97 seconds, or at any other suitable times. These one or morecurrent values measured within different time-segments can then be usedto determine analyte concentration based on calibration informationassociated with the different time-segments. For example, a low analyteconcentration can be determined during an early time-segment, such as afirst time-segment, based on calibration data associated with the lowanalyte concentration. Conversely, a high analyte concentration can bedetermined during a later time-segment, such as a second time-segment,based on calibration data associated with the higher analyteconcentration.

In some embodiments, calibration data can be described by a plurality ofcalibration curves, wherein each time-segment can be associated with aplurality of calibration curves. For example, a first time-segment canbe associated with a plurality of first calibration curves. Each firstcalibration curve can be associated with a first time-segment andvarious other variables, such as, for example, hematocrit. Othercalibration curves may be associated with temperature, sample, teststrip 10, or meter 100.

As explained in detail below, a first set of calibration data caninclude a plurality of calibration curves representing data associatedwith blood samples of different levels of hematocrit. Specifically, onecurve may be associated with a low level of hematocrit, another curvewith a medium level of hematocrit, and a third curve with a high levelof hematocrit. In some instances only two calibration curves may beused. These plurality of calibration curves may be associated with afirst time-segment, while another set of calibration data may beassociated with a second time-segment.

In some embodiments, the plurality of calibration curves associated witha first time-segment can be used to determine a plurality of firstanalyte concentrations. For example, a plurality of calibration curvesrepresenting different levels of hematocrit may be used to determine aplurality of analyte concentrations associated with these differentlevels of hematocrit. Further, a plurality of calibration curvesassociated with a second time-segment can be used to determine aplurality of second analyte concentrations. In some embodiments, two,three, four, or more, calibration curves can be used to determineanalyte concentration, wherein each calibration curve can be associatedwith a corresponding time-segment. Two, three, four, or more,time-segments may be used by the following method.

FIG. 3 depicts a method 200 for determining analyte concentration,according to an exemplary embodiment of the present disclosure.Initially, a potential excitation can be applied to a fluid samplecontaining an analyte (Step 210). For example, the fluid sample may becontained within test strip 10. As described above, meter 100 can beconfigured to supply the potential excitation across electrodes 22, 24within test strip 10. Meter 100 could also be configured to measure acurrent during one or more time-segments following application of thepotential excitation. Meter 100 may then determine an analyteconcentration, such as, for example, a blood glucose level, based onmultiple sets of calibration data associated with the time-segments.Other test strips, meters, or analyte monitoring systems could also beconfigured to incorporate method 200.

Following application of a potential excitation, a first current may bemeasured, wherein the first current can be measured during a firsttime-segment (Step 220). As described above, a first time-segment caninclude a time period of less than about two to ten seconds.

Next, the first current may be compared with a first target-range (Step230). Specifically, the first measured current can be compared with afirst target-range of current values associated with a first range ofanalyte concentrations. If the measured current is within the firsttarget-range, then the analyte concentration of the fluid sample shouldalso be within the corresponding first range of analyte concentrations.For example, a first range of glucose concentrations may be less thanabout 50, 100, or 150 mg/dL. Each different range may also be associatedwith a corresponding target-range of current values. If the measuredcurrent value falls within any of these target-ranges, then a glucoseconcentration within a first range of 50, 100, or 150 mg/dL can bepresumed. Specifically, a final analyte concentration may be determinedbased on the data associated with the first time-segment (Step 240).This data may include the first measured current value obtained by Step220, and various other data associated with the first time-segment.Various methods for determining analyte concentration are known in theart. In some embodiments, these calculations can use data associatedwith the first calibration data.

To illustrate by way of an example, the first target-range may include amaximum glucose concentration of 50 mg/dL. Generally, lower analyteconcentrations are associated with earlier time-segments, and higheranalyte concentrations are associated with later time-segments. If thefirst measured current is about equal to or less than 50 mg/dL, method200 may determine an analyte concentration based on the data associatedwith the first time-segment. Such a determination may include the use ofone or more sets of calibration data associated with the firsttime-segment, as explained below. Various target-ranges, calibrationdata, time-segments, and other parameters can be determined empiricallyand can vary depending on test strip and meter design, manufacturingconditions, fluid type, operating conditions, etc.

In some embodiments, a suitable range of analyte concentrations may beencompassed by two or more target-ranges. For example, a firsttarget-range could be associated with a glucose concentration of about10 to about 50 mg/dL, and a second target-range could be associated witha glucose concentration above about 50 mg/dL. The limits of varioustarget-ranges may overlap, and a third, fourth, or any other number oftarget-ranges could also be used.

Various calibration data may be associated with a time-segment, wherebycalibration data could include any suitable information associated witha time-segment. For example, a calibration curve may representcalibration data corresponding to an analyte concentration between alower and an upper analyte concentration. Such an association can permituse of concentration-dependent calibration information. For example, oneset of calibration information may exist for low analyte concentrationswhile another set of calibration information may exist for high analyteconcentrations. In some embodiments, two, three, four or more differentsets of calibration data could be used to determine analyteconcentration, whereby the different sets of calibration data eachcorrespond to different ranges of analyte concentrations.

Each set of calibration data could include empirical data associatedwith a range of analyte concentrations, termed “calibration-range.” Insome embodiments, the calibration-range may be different to thecorresponding target-range. For example, a first target-range may beassociated with a glucose concentration of about 10 to 50 mg/dL, while afirst calibration-range may be associated with a glucose concentrationof about 0 to 75 mg/dL. A calibration-range larger than thecorresponding target-range can permit greater accuracy in determiningthe corresponding calibration data as a greater range of empirical datacould be used to determine the calibration data. Also, as explained indetail below, adjacent calibration-ranges can overlap and provideadditional data for determining a set of calibration data.

In some embodiments, two calibration-ranges can be used to determine twosets of calibration data. For example, a first calibration-range can beassociated with a glucose concentration of about 0 to about 75 mg/dL,and a second calibration-range can be associated with a glucoseconcentration of above about 50 mg/dL. In some embodiments, the secondcalibration-range can be associated with a glucose concentration ofabout 30 mg/dL to about 240 mg/dL, and a third calibration-range can beassociated with a glucose concentration of about 75 to about 600 mg/dL.In yet other embodiments, a fourth calibration-range could be used. Forexample, a third time-segment could be triggered at about nine seconds,a fourth time-segment at about fourteen seconds, a third target-rangecould be associated with a glucose concentration of about 350 mg/dL, anda fourth target-range with a glucose concentration of about 600 mg/dL.Also, each particular set of calibration data can be associated with acorresponding calibration-range such that a first set of calibrationdata is associated with a first calibration-range, a second set ofcalibration data is associated with a second calibration-range, and soforth.

For example, FIG. 4A depicts a chart 400 representing two sets ofcalibration data used for determining a glucose concentration. Aplurality of first calibration curves 410 can be associated with a firsttime-segment (t₁). As illustrated, three calibration curves are shownfor t₁, although two, four, five, or more curves could be determined orused. First set of calibration curves 410 can be associated with t₁, afirst measured current x₁, or any other variable associated with a firsttime-segment. In some embodiments, the first set of calibration curvescan be determined using a first calibration-range of glucoseconcentrations, such as, for example, 0 to 75 mg/dL.

A plurality of second calibration curves 420 can be associated with asecond time-segment. As previously described for first set ofcalibration curves 410, second set of calibration curves 420 can includetwo, four, five, or more curves. As shown, second set of calibrationcurves 420 is associated with second time-segment t₂, and secondmeasured current x₂, although other variables could be used. In someembodiments, second set of calibration curves 420 can be determinedusing a second calibration-range of glucose concentrations, such as, forexample, above about 50 mg/dL. Although not shown in FIG. 4A, a third,fourth, or other sets of calibration curves could also be determinedand/or used in conjunction with method 200.

FIG. 4A shows individual calibration curves with generally curved anddifferent slopes. In other embodiments, such curves may be generallylinear, and may or may not have different slopes. Also, these variouscalibration curves could also be represented in any suitable dataformat. Such calibration data could include any suitable representationof calibration information, such as, for example, slopes, relationships,charts, tables, equations, algorithms, or data formats. Variousequations could include quadratic, polynomial, data-fitted, or othermathematical descriptions. Calibration data could include strip, lot, ormeter specific information, and may account for hematocrit, temperature,pH, or other variations in testing conditions, analyte type, orphysiological sample. Such calibration data may be encoded on strip 10or within meter 100.

An event triggering Step 230 could include any suitable event. Forexample, a set period of time could elapse after applying the excitationpotential in Step 210. Or, once the end of a first time-segment isreached, such as four seconds, Step 230 could be triggered. In otherembodiments, a current reading could trigger the event. For example,Step 230 could be triggered if a measured current drops below apredefined level, such as, 3 mA. Certain values, or value ranges, ofvoltage, impedance, or other parameters associated with variouselectrochemical techniques could also be used as a triggering event.

As shown in method 200, if the first measured current is not within thefirst target-range, method 200 may then continue to measure a secondcurrent during a second time-segment (Step 250). Specifically, one ormore current measurements can occur at a time following that of thefirst current measurement. For example, one or more additional currentmeasurements could occur in a second time-segment, such as, about twoseconds to ten seconds following excitation application in Step 210.Step 250 could occur as described above for Step 220, measurement of thefirst current during a first time-segment.

Following Step 250, data associated with the first and secondtime-segments can be compared. Such data can include analyteconcentration, current, voltage, or any other suitable information. Thecomparison of first and second time-segment data can include determiningif such data are about equal (Step 260). Method 200 may require suchdata be equal, or about equal, whereby the difference can be within anacceptable range, such as, for example, 10%, 5%, or 2% variation.

If the first and second time-segment data are about equal, the finalanalyte concentration can be determined based on the measured data (Step270). This data could include the first or second time-segment data, orsome combination. Such a determination could be similar to thedetermination described above for Step 240. By way of example, FIG. 4Ashows an exemplary embodiment whereby the first and second-segment dataare about equal.

FIG. 4A shows two sets of calibration data on chart 400, plotted as aseries of curves on the axes of analyte concentration (mg/dL) axis andcurrent (nA). First plurality of calibration curves 410 as shownincludes three curves G(t₁, H₁), G(t₁, H₂), and G (t₁, H₃). In otherembodiments, calibration curves 410 could include two, four, or morecurves. Alternatively, these or other calibration data could berepresented by a table, graph, or equation, and may include empiricaland/or modeled data.

As shown, each calibration curve 410 represents calibration dataassociated with a first time t₁, whereby t₁ is a time point within thefirst time-segment. For example, t₁ could be 2, 4, or 6 secondsfollowing application of a potential. Calibration curves 410 can alsorepresent calibration data associated with one or more variables, suchas, for example, hematocrit or temperature. As shown, each calibrationcurve 410 represents calibration data associated with three differentlevels of hematocrit, H₁, H₂, H₃. For example, H₁ could represent a lowhematocrit level, H₂ could represent a physiological hematocrit level,and H₃ could represent a high hematocrit level. Each calibration curvecould also include data associated with additional or other variables asrequired. Further, different calibration curves may be required fordifferent test strips 10, or meters 100.

Chart 400 also shows second plurality of calibration curves 420 asincluding three curves G(t₂, H₁), G(t₂, H₂), and G (t₂, H₃). As notedabove, in other embodiments calibration curves 420 could include two,four, or more curves. As shown, each calibration curve 420 representscalibration data associated with a second time t₂, whereby t₂ is a timepoint within the second time-segment. For example, t₂ could be 6, 8, 10or 12 seconds following application of a potential. As describedpreviously, each calibration curve 420 represents calibration dataassociated with three different levels of hematocrit, H₁, H₂, H₃,although data associated with other variables are also contemplated.

As outlined above, a first current x₁ can be measured during a firsttime-segment, as described in Step 220. Initially, method 200 may assumethe fluid sample has a physiological hematocrit level, i.e., H₂.Referring to FIG. 4A, a first current x₁, measured at time t₁, andassuming H₂ hematocrit level, results in an analyte concentration of y₁.If required, a second current x₂ can be measured during a secondtime-segment, as described in Step 250.

Referring to FIG. 4A, a second current x₂, measured at time t₂, andassuming H₂ hematocrit level, results in an analyte concentration of y₂.As shown in FIG. 4A, both y₁ and y₂ are about equal. Such a result maythen indicate that a final analyte concentration may be determined basedon the measured data, as in Step 270. A different calculation may berequired if the first and second time-segment data are not sufficientlyequal or the difference between the data is not within an acceptablerange.

Returning to FIG. 3, following comparison of first and secondtime-segment data in Step 260, and determination that the data are notabout equal, method 200 can then calculate first and second analyteconcentrations (Step 280). Various methods for determining analyteconcentration are known in the art. In some embodiments, thesecalculations can use data associated with the first and second sets ofcalibration data. If the first and second analyte concentrations areabout equal, a determination of a final analyte concentration can bemade as described above for Step 270 and as illustrated by FIG. 4A. Ifsuch analyte concentrations are not equal, or within a suitable range,then both sets of calibration data may be required to accuratelydetermine a final analyte concentration, as shown in FIG. 4B.

FIG. 4B illustrates an exemplary embodiment similar to FIG. 4A, showinga chart 500 that includes a first plurality of calibration curves 510and a second plurality of calibration curves 520. First current x₁ mayinclude a plurality of analyte concentrations associated with firstplurality of calibration curves 510. Specifically, first current x₁ mayinclude analyte concentrations a₁, a₂, and a₃, associated withcalibration curves G(t₁, H₃), G(t₁, H₂), and G(t₁, H₁) respectively.That is, at first current x₁, using calibration curve G(t₁, H₃) resultsin analyte concentration value a₁, while using calibration curve G(t₁,H₁) results in analyte concentration value a₃. Similarly, second currentx₂ may include analyte concentrations b₁, b₂, and b₃, associated withcalibration curves G(t₂, H₃), G(t₂, H₂), and G(t₂, H₁) respectively. Asshown in FIG. 4B, three calibration curves are used, although two, four,five, or more calibration curves could also be used.

Method 200 may be used with any calibration data displaying a generalconvergence property. For example, here data associated with a firsttime-segment (data associated with x₁) displays convergence at a secondtime-segment (data associated with x₂). Specifically, (b₁−b₃) thedifference between the upper and lower bounds of calibration dataassociated with x₂, is less than (a₁−a₃), the upper and lower bounds ofcalibration data associated with x₁. An iterative method, based on thevarious analyte concentration values associated with differentcalibration data, can be used to determine a final analyte concentrationbased on this convergence property, i.e., (b₁−b₃)<(a₁−a₃).

One method of using the property of convergence will be described by wayof example, although other convergence-based methods are alsocontemplated. Initially, an average of a set of calibrated data (e.g., amedian calibration curve), could be assumed. For example, as shown inFIG. 4B, an average hematocrit value (H₂) could be initially assumed andused to calculate an initial analyte concentration. At a first currentx₁, such an initial analyte concentration is represented by a₂. Asexplained above, if the second current x₂ results in an analyteconcentration of b₂, whereby a₂ is about equal to b₂, then a finalanalyte concentration can be determined based on either x₁ or x₂ data.However, a₂ and b₂ may not be sufficiently converged whereby thedifference between a₂ and b₂ is beyond a desired degree of accuracy,such as, for example, 5%.

If the difference between the initial calibration data associated withfirst and second time-segments is too large, then various algorithmictechniques can be used to optimize the determination of a final analyteconcentration (Step 290). For example, assume a first estimate ofanalyte concentration is b₂, and (a₂−b₂) is outside an acceptable value.As shown in FIG. 4B, a revised hematocrit value may be calculated basedon a₀, the analyte concentration corresponding to b₂ but associated withfirst current x₁. The analyte concentration associated with firstcurrent x₁ may be modified by interpolating between a₁, a₂, or a₃. Asshown in FIG. 4B, a₀ is between a₁ and a₂. A revised hematocrit value(HCT) could be determined based on the following equation:

$\frac{\left( {{HCT} - H_{2}} \right)}{\left( {H_{1} - H_{2}} \right)} = \frac{\left( {a_{0} - a_{2}} \right)}{\left( {a_{1} - a_{2}} \right)}$

The equation could be solved for HCT as the other variables are allknown.

Following determination of the revised hematocrit value based on thecalibration data associated with first current x₁, the analyteconcentration y₂ could then be revised. For example, the revised valuey₃ could be determined based on the following equation:

$\frac{\left( {y_{3} - y_{2}} \right)}{\left( {b_{1} - b_{2}} \right)} = \frac{\left( {a_{0} - a_{2}} \right)}{\left( {a_{1} - a_{2}} \right)}$

The equation could be solved for y₃ as the other variables are allknown. Further, in some embodiments this process could be repeated asnecessary to revise one or both of the values of analyte concentrationand hematocrit, or any other calibration variable.

Various algorithms could be used to solve for one or more variablesassociated with calibration data. For example, the techniques describedabove could be used in combination with iterative methods to refine acalculation to within a desired accuracy range. Other optimizationtechniques are also known in the art, such as, for example,extrapolation or interpolation using linear or non-linear methods.Extrapolation methods could be used to determine appropriate calibrationdata beyond a given data range. Interpolative methods may capcalibration data within upper or lower values, or naturally extend(extrapolate) data depending upon variance or bias statistics associatedwith actual data. For example, as explained above, the analyteconcentration may be modified by extrapolating beyond a₁, a₂, or a₃.

The method described above can use two or more sets of calibration data,whereby each set of calibration data can be associated with a differenttime-segment, to solve for two or more unknown variables. A set ofcalibration data can include a lookup table, array, mathematicalequation, or any other suitable data structure configured to representcalibration data, and various multiple types of calibration data couldbe used with method 200. In particular, method 200 can use at least twosets of calibration curves, wherein each plurality of calibration curvescan be associated with a different time-segment. For example, method 200could use three sets of calibration data associated with any threesuitably defined time-segments, such as, about four, seven, and tenseconds. Each time-segment may include specifically derived calibrationdata associated with that particular time-segment.

Method 200 can be used to solve for two unknown variables, such as, forexample, hematocrit and analyte concentration as outline above. In otherembodiments, three or more different variables could also be solved. Forexample, a third variable, such as, temperature, may also be included ina set of calibration data. In some embodiments, at low hematocrit levelsan analyte concentration may be relatively less temperature dependentthan high hematocrit levels which may be relatively more temperaturedependent. Additional calibration data and/or time-segments may be usedto solve for a temperature variable using the methods described herein.

Another mathematical method that may be employed with method 200 usestwo or more line-plots generated from data associated with two or moretime-segments. As described previously, multiple calibration curves maybe generated for multiple time-segments, as shown in FIG. 4A. Then, dataassociated with each time-segment may be determined based on one or moremeasured current values. As shown in FIG. 4B, current value x₁ mayinclude the associated data a₁, a₂, and a₃, while current value x₂ mayinclude the associated data b₁, b₂, and b₃. These two sets of associateddata may then be plotted to form two line-plots, as shown in FIG. 4C.

FIG. 4C illustrates an exemplary embodiment showing a chart 600, thatincludes a first line-plot 610 and a second line-plot 620. Firstline-plot 610 may include a₁, a₂, a₃, or other data associated with x₁,while second line-plot 620 may include b₁, b₂, b₃, or other dataassociated with x₂. The resulting line-plots may be plotted with respectto two unknown variables, such as, for example, glucose concentration(mg/dL) and hematocrit (%), as shown in FIG. 4C. These resultingline-plots may be generally linear, curved, or irregular in form, andgenerally intersect about a point 630. Point 630 may be generallydefined if the two line-plots are irregular, or if the line-plots showgeneral intersection about a limited range of values. Consequently,point 630 may encompass a limited range of values of one or more unknownvariables. Based on point 630, a predicted glucose concentration (G_(P))and a predicted hematocrit (H_(P)) can be readily determined. As point630 may encompass a range of values, so to may G_(P) or H_(P) encompassa limited range of values.

Another embodiment consistent with the principles of this inventionextrapolates a current decay measured during a first time-segment todetermine a current value at much longer time. This can be achieved byformulating an extrapolation algorithm using experimental data at longertest times. The extrapolated current, or “predicted current,” can becorrelated to analyte concentration with improved accuracy andprecision. If the calculated analyte concentration is outside apre-determined range, the measurement continues to the secondtime-segment, similar to the method previously described. Theextrapolation algorithm and analyte concentration determination can bedetermined using any number of sets of calibration data, as previouslydescribed. Further, this method can be repeated until the entiremeasurement range is covered.

FIG. 5 depicts a chart 300 of three different fluid samples showingcurrent decay curves over time after the application of a potentialexcitation. While the three samples depicted contain similar glucoseconcentrations, all three samples contain different amounts of red bloodcells, i.e. different hematocrit values. The sample with the lowesthematocrit value is depicted by a line 310, and has the steepest slopeover an indicated dashed range 350. In contrast, the sample with thehighest hematocrit value is depicted by a line 330, and has the flattestslope over indicated range 350. A line 320 represents a sample with anintermediate hematocrit value. As shown, all three lines 310, 320, 330approximately converge toward a generally common current value at afuture time-point, as depicted by a region 340.

In some embodiments, glucose concentration can affect the shape of acurrent decay curve. For example, different hematocrit values can affectthe convergence of current decay curves. In particular, samplescontaining lower glucose concentrations may reach a generally commoncurrent value faster than samples containing higher glucoseconcentrations. As such, current decay curves representing samplescontaining lower glucose concentrations may converge in a shorter timeperiod than samples containing higher glucose concentrations.Determining a wide range of glucose concentrations could require two ormore time-segments, and calibration data associated with different timesegments may be different.

In another embodiment, extrapolation techniques could be applied to oneor more time-segments of one or more current decay curves to determine agenerally common current value that could be reached at a longer time.For example, data associated with slope information of a single decaycurve could be used to determine a future current value or associatedtime value. Data contained within dashed range 350 could beextrapolated, using linear or other curve fitting techniques, todetermine a current associated with region 340. Such a technique offersanother method of determining glucose concentration within a shortertest time. Also, such slope or other relational data could be used inassociation with any one or more time-segments.

To determine slope information associated with a current decay curve,current data from two or more current measurements associated with twotime-points may be obtained as previously described. These current datamay then be fit with appropriate mathematical equations configured toprovide a predicted current value at some future time-point. Forexample, an illustrative method could include measuring a first currentvalue associated with the potential excitation at a first time-point andmeasuring a second current value associated with the potentialexcitation at a second time-point. The method could then determine apredicted current at a future time-point, wherein the predicted currentcould be determined using an extrapolated current decay curve based onthe first and second current values. Analyte concentration could then becalculated based on the predicted current and dynamically-selectedcalibration data, as described above.

In some embodiments, the extrapolated current decay curve could beselected from a plurality of extrapolated current decay curves. Theseextrapolated current decay curves could be based on empirical data, orobtained using any suitable method known in the art. Such current decaycurves may also be associated with one or more time-segments, analyteconcentrations, or other parameters previously discussed.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1. A method for analyzing an analyte, the steps comprising: applying apotential excitation to a fluid sample containing an analyte; measuringa first current during a first time-segment following application of thepotential excitation; measuring a second current during a secondtime-segment following application of the potential excitation;calculating a plurality of first analyte concentrations based on thefirst measured current and a first set of calibration data associatedwith the first time-segment; and calculating a plurality of secondanalyte concentrations based on the second measured current and a secondset of calibration data associated with the second time-segment.
 2. Themethod of claim 1, further comprising: determining a final analyteconcentration based on at least one of the first analyte concentrationsand at least one of the second analyte concentrations.
 3. The method ofclaim 2, wherein determining the final analyte concentration includes atleast one of an iterative algorithm, an interpolative algorithm, aline-plot, and an extrapolative algorithm.
 4. The method of claim 1,wherein a set of calibration data includes at least one of a pluralityof calibration curves, a lookup table, a data array, and a mathematicalequation.
 5. The method of claim 1, wherein the first time-segment isless than about two to ten seconds, and the second time-segment is morethan about two to ten seconds.
 6. The method of claim 5, wherein thefirst time-segment is less than about eight seconds, and the secondtime-segment is more than about eight seconds.
 7. The method of claim 1,wherein at least part of the calibration data is determined using atleast one of empirical data, and predicted data.
 8. The method of claim1, wherein the first and second sets of calibration data are associatedwith different levels of hematocrit.
 9. The method of claim 8, whereinthe different levels of hematocrit include at least one of a high levelgreater than about 42%, a physiological level of about 42%, and a lowlevel less than about 42%.
 10. The method of claim 1, furthercomprising: measuring a third current during a third time-segmentfollowing application of the potential excitation; calculating aplurality of third analyte concentrations based on the third measuredcurrent and a third set of calibration data associated with the thirdtime-segment; and determining a final analyte concentration based on atleast one of the first and second analyte concentrations and at leastone of the third analyte concentrations.
 11. The method of claim 1,wherein the analyte is glucose and the fluid sample includes blood. 12.The method of claim 1, wherein the fluid sample includes an enzyme of atleast one of glucose oxidase and glucose dehydrogenase and a mediator ofat least one of potassium ferricyanide and ruthenium hexamine.
 13. Asystem for analyzing an analyte in a fluid sample, comprising: a set ofelectrodes configured to apply a potential excitation to a fluid samplecontaining an analyte; a processor configured to: measure a firstcurrent during a first time-segment following application of thepotential excitation; measure a second current during a secondtime-segment following application of the potential excitation;calculate a plurality of first analyte concentrations based on the firstmeasured current and a plurality of first calibration curves associatedwith the first time-segment; and calculate a plurality of second analyteconcentrations based on the second measured current and a plurality ofsecond calibration curves associated with the second time-segment. 14.The system of claim 13, further comprising a processor configured to:determine a final analyte concentration based on at least one of thefirst analyte concentrations and at least one of the second analyteconcentrations.
 15. The system of claim 14, wherein the final analyteconcentration is determined using at least one of an iterativealgorithm, an interpolative algorithm, a line-plot, and an extrapolativealgorithm.
 16. The system of claim 14, wherein the system is furtherconfigured to a display a value representative of the final analyteconcentration.
 17. The system of claim 13, wherein the firsttime-segment is less than about two to ten seconds, and the secondtime-segment is more than about two to ten seconds.
 18. The system ofclaim 13, wherein the first time-segment is less than about eightseconds, and the second time-segment is more than about eight seconds.19. The system of claim 13, wherein at least one of the calibrationcurves is determined using at least one of empirical data, and predicteddata.
 20. The system of claim 13, wherein the plurality of first andsecond calibration curves are associated with different levels ofhematocrit.
 21. The system of claim 20, wherein the different levels ofhematocrit include at least one of a high level greater than about 42%,a physiological level of about 42%, and a low level less than about 42%.22. The system of claim 13, further comprising a processor configuredto: measure a third current during a third time-segment followingapplication of the potential excitation; calculate a plurality of thirdanalyte concentrations based on the third measured current and aplurality of third calibration curves associated with the thirdtime-segment; and determine a final analyte concentration based on atleast one of the first and second analyte concentrations and at leastone of the third analyte concentrations.
 23. The system of claim 13,wherein the analyte is glucose and the fluid sample includes blood. 24.The system of claim 13, wherein the fluid sample includes an enzyme ofat least one of glucose oxidase and glucose dehydrogenase and a mediatorof at least one of potassium ferricyanide and ruthenium hexamine. 25.The system of claim 13, wherein the set of electrodes are containedwithin in a test strip.
 26. The system of claim 13, wherein theprocessor is contained within a meter.
 27. A computer readable media,wherein the media comprises a plurality of instructions configured todirect a processor to: measure a first current during a firsttime-segment following application of a potential excitation, whereinthe potential excitation is applied to a fluid sample containing ananalyte; measure a second current during a second time-segment followingapplication of the potential excitation; calculate a plurality of firstanalyte concentrations based on the first measured current and a firstset of calibration data associated with the first time-segment; andcalculate a plurality of second analyte concentrations based on thesecond measured current and a second set of calibration data associatedwith the second time-segment.
 28. The computer readable media of claim27, wherein the instructions further direct the processor to: determinea final analyte concentration based on at least one of the first analyteconcentrations and at least one of the second analyte concentrations.29. The computer readable media of claim 28, wherein the final analyteconcentration is determined using at least one of an iterativealgorithm, an interpolative algorithm, a line-plot, and an extrapolativealgorithm.
 30. The computer readable media of claim 27, wherein thecalibration data includes at least one of a plurality of calibrationcurves, a lookup table, a data array, and a mathematical equation. 31.The computer readable media of claim 27, wherein the first time-segmentis less than about two to ten seconds, and the second time-segment ismore than about two to ten seconds.
 32. The computer readable media ofclaim 31, wherein the first time-segment is less than about eightseconds, and the second time-segment is more than about eight seconds.33. The computer readable media of claim 27, wherein at least part ofthe calibration data is determined using at least one of empirical data,and predicted data.
 34. The computer readable media of claim 27, whereinthe first and second sets of calibration data are associated withdifferent levels of hematocrit.
 35. The computer readable media of claim34, wherein the different levels of hematocrit include at least one of ahigh level greater than about 42%, a physiological level of about 42%,and a low level less than about 42%.
 36. The computer readable media ofclaim 27, wherein the instructions further direct the processor to:measure a third current during a third time-segment followingapplication of the potential excitation; calculate a plurality of thirdanalyte concentrations based on the third measured current and a thirdset of calibration data associated with the third time-segment; anddetermine a final analyte concentration based on at least one of thefirst and second analyte concentrations and at least one of the thirdanalyte concentrations.